Lynx-TinySync-0.6B

Lynx-TinySync-0.6B is a lightweight, high-performance model designed for mathematical reasoning, code generation, and general-purpose inference. Built on a custom modular dataset and powered by an efficient architecture, it excels in delivering structured, accurate outputs even in mid-resource environments. Despite its compact 0.6B parameter size, it demonstrates remarkable proficiency in math, code, and technical language understanding.

GGUF: https://huggingface.co/prithivMLmods/Lynx-TinySync-0.6B-GGUF

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Key Features

1. Custom Modular Dataset Training Fine-tuned using a handcrafted blend of math, code, and reasoning datasets, ensuring high performance in symbolic tasks and general queries.

2. Mathematical Reasoning Handles algebra, calculus, geometry, and symbolic logic with clarity—ideal for tutoring, educational support, and math competitions.

3. Compact Code Assistant Generates clean, efficient code in Python, JavaScript, and more—complete with explanations and bug-fix breakdowns.

4. Structured Output Generation Outputs in JSON, Markdown, LaTeX, and tabular formats—well-suited for documentation, structured data templates, and technical content.

5. Multilingual Technical Reasoning Supports math and code queries in 20+ languages with consistent output—enabling multilingual academic and professional use cases.

6. Optimized for Low-Resource Deployment With only 0.6B parameters, it's ideal for inference on edge devices, local machines, and GPU-constrained environments.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Lynx-TinySync-0.6B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve the equation: 2(x - 4) + 3 = 11. Show all steps."

messages = [
    {"role": "system", "content": "You are a step-by-step math tutor."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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Intended Use

• Mathematical problem solving and symbolic logic
• Lightweight code generation and debugging
• Generation of structured content (e.g., JSON, LaTeX, Markdown)
• Educational support across languages and domains
• Low-resource deployment in academic or field settings

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Limitations

• May underperform on long-form creative generation tasks
• Smaller context window may limit deep multi-turn reasoning
• Less capable in adversarial or abstract reasoning queries
• Technical multilingual use focused—general dialogue fluency limited

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References

1. Qwen2.5 Technical Report
2. YaRN: Efficient Context Window Extension of Large Language Models